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Remote Sens. 2014, 6(10), 9213-9238;

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia
CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
CSIRO Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia
School of Earth and Environment, University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia
Author to whom correspondence should be addressed.
Received: 12 August 2014 / Revised: 11 September 2014 / Accepted: 11 September 2014 / Published: 26 September 2014
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The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm. View Full-Text
Keywords: data fusion; blending; STARFM; ESTARFM; multispectral indices data fusion; blending; STARFM; ESTARFM; multispectral indices

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jarihani, A.A.; McVicar, T.R.; Van Niel, T.G.; Emelyanova, I.V.; Callow, J.N.; Johansen, K. Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches. Remote Sens. 2014, 6, 9213-9238.

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